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Sparsity Models for Signals: Theory and Applications
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Raja Giryes, Ph.D. Thesis Seminar
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Tuesday, 15.10.2013, 11:30
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Taub 337
Many signal and image processing applications have benefited remarkably from the theory of sparse representations. In the classical synthesis model, the signal is assumed to have a sparse representation under a given dictionary. In this work we focus on greedy methods for the problem of recovering a signal from a set of deteriorated linear measurements. We consider four different sparsity frameworks that extend the aforementioned synthesis model that target the signal's representation: (i) The cosparse analysis model; (ii) the signal space paradigm; (iii) the transform domain strategy; and (iv) and the sparse Poisson noise model. In the first part of the talk we present extensions for greedy-like algorithms for the synthesis and the first three alternative models. In the second part we consider the Poisson denoising problem with a new Poisson statistics based sparsity model achieving state-of-the-art-results.
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